Afterwards, we apply different computations to this data matrix: (i) the data matrix is considered as is, (ii) its dimensions are reduced by applying PCA [1]}, and (iii) a subset of its features is selected by applying the proposed GA for feature selection (Section ). The resulting three data matrices are passed to the learning model of interest (SVM or K-means clustering) to compare the three strategies. Finally, we remind that the first two strategies represent the benchmark.
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